Applicable to photovoltaic new energy power system power storage optimization control system

Abstract

The present invention discloses an electric power storage optimization control system applicable to a photovoltaic new energy power system, relates to the technical field of electric power storage optimization, and solves the problem that the current electric power storage control system fails to accurately predict the power demand of users in the future, resulting in overcharging or overdischarging of energy storage batteries, which affects the battery life. The present invention comprises an electric power storage module, an electric power dispatching module, a demand forecasting module and a health monitoring module. The health monitoring module is used for real-time monitoring of the health status corresponding to the electric power storage module, and monitoring and analyzing the electric power storage data of the electric power storage module; the demand forecasting module is used for analyzing the historical power consumption of the electric power storage module; the electric power dispatching module is used for dynamically adjusting the electric energy storage amount of the electric power storage module according to the power demand forecasting data, and obtaining the charging value or discharging value of the electric power storage module. The present invention realizes the optimization control of the electric power storage of the photovoltaic new energy power system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power storage optimization, and in particular is applicable to a power storage optimization control system for a photovoltaic new energy power system. Background Art

[0002] Electric energy storage technology mainly refers to the storage of electric energy. The stored energy can be used as emergency energy, or it can be used to store energy when the grid load is low, and output energy when the grid load is high, which is used to reduce peak loads and fill valleys and reduce grid fluctuations. Energy comes in many forms, including radiation, chemical, gravitational potential energy, electric potential energy, electricity, high temperature, latent heat and power. Energy storage involves converting energy in a form that is difficult to store into a more convenient or economically storable form.

[0003] In the prior art, the power storage control system fails to accurately predict the user's power demand in the future, resulting in overcharging or overdischarging of the energy storage battery, which affects the battery life. Moreover, the system is not optimized according to the actual health status, which shortens the service life of the energy storage battery. At the same time, it reduces the overall efficiency of the power storage control system and increases maintenance costs.

[0004] To this end, the present invention proposes an electricity storage optimization control system suitable for photovoltaic new energy power system. Summary of the invention

[0005] The purpose of the present invention is to propose an electricity storage optimization control system suitable for photovoltaic new energy power system to solve the problems raised in the above background technology.

[0006] The technical problems to be solved by the present invention are:

[0007] How to optimize the control of electricity storage in photovoltaic new energy power systems.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] Applicable to photovoltaic new energy power system power storage optimization control system, including data acquisition module, power storage module, power dispatch module, demand forecasting module, health monitoring module, database module, photovoltaic power generation terminal and management platform;

[0010] The photovoltaic power generation terminal is used to convert light energy into electrical energy and transmit the converted electrical energy to the electrical energy storage module; the electrical energy storage module is used to store the electrical energy transmitted by the photovoltaic power generation terminal;

[0011] The data acquisition module is used to collect the electric energy storage data of the electric energy storage module, and send it to the health monitoring module through the management platform; the database module is used to store the standard voltage data, standard temperature data and standard battery capacity data of the electric energy storage module; the health monitoring module is used to monitor the health status of the electric energy storage module in real time, and monitor and analyze the electric energy storage data of the electric energy storage module, and obtain the health status of the electric energy storage module and send it to the management platform, and the management platform is used to maintain the electric energy storage module according to the health status of the electric energy storage module;

[0012] The data acquisition module is also used to collect the historical power consumption of the electric energy storage module, and upload the historical power consumption to the management platform after adding a timestamp, and the management platform sends the historical power consumption with the timestamp to the demand forecasting module;

[0013] The demand forecasting module is used to analyze the historical power consumption of the power storage module, and obtain the power demand forecasting data of the power storage module through the analysis, and the power demand forecasting data is sent to the power dispatching module through the management platform; the data acquisition module is also used to collect the real-time photovoltaic power generation of the photovoltaic power generation terminal, and the real-time photovoltaic power generation is sent to the power dispatching module through the management platform;

[0014] The electric energy scheduling module is used to dynamically adjust the electric energy storage capacity of the electric energy storage module according to the power demand forecast data, obtain the charging value or discharging value of the electric energy storage module and upload it to the management platform, and the management platform is used to check the charging and discharging status of the electric energy storage module based on the charging value or discharging value.

[0015] Furthermore, the electric energy storage data of the electric energy storage module includes real-time current data, real-time temperature data, real-time voltage data and real-time battery capacity data of the electric energy storage module.

[0016] Furthermore, the monitoring and analysis process of the health monitoring module is as follows:

[0017] Obtaining real-time current data SSI, real-time voltage data SSV and standard voltage data BZV of the electric energy storage module;

[0018] The internal resistance degradation index NZZ of the large energy storage battery in the electric energy storage module is calculated by the formula NZZ=|BZV-SSV| / SSI;

[0019] Obtaining real-time temperature data SST and standard temperature data BZT of the electric energy storage module;

[0020] The temperature health index WDJ of the large energy storage battery in the energy storage module is calculated by the formula WDJ = SST / BZT;

[0021] Obtain real-time battery capacity data SSR and standard battery capacity data BZR of the electric energy storage module;

[0022] The capacity health index JKR of large energy storage batteries is calculated using the formula JKR=SSR / BZR.

[0023] Furthermore, the monitoring and analysis process of the health monitoring module also includes:

[0024] The comprehensive health index ZHJ of the energy storage module is calculated by the formula ZHJ=w1×JKR+w2×(1 / NZZ)+w3×WDJ; where w1, w2 and w3 are weight coefficients with fixed values, and w1>w2>w3;

[0025] When the comprehensive health index of the electric energy storage module is greater than or equal to a first health threshold, determining that the health status of the electric energy storage module is a healthy status;

[0026] When the comprehensive health index of the electric energy storage module is greater than or equal to the second health threshold and less than the first health threshold, it is determined that the health status of the electric energy storage module is in a sub-healthy state;

[0027] When the comprehensive health index of the electric energy storage module is less than a second health threshold, determining that the health status of the electric energy storage module is in a risky state;

[0028] The first health threshold is greater than the second health threshold, and the second health threshold is greater than zero.

[0029] Furthermore, the historical electricity consumption with timestamp is specifically as follows: the past year is divided into several continuous time periods, and then the power output data of the power storage module in each time period is obtained, and the power output data of each time period is combined to obtain the historical electricity consumption of the power storage module.

[0030] Furthermore, the analysis process of the demand forecasting module is as follows:

[0031] Normalize the historical electricity consumption;

[0032] Perform outlier detection on normalized historical electricity consumption;

[0033] If there is extreme historical electricity consumption in the normalized historical electricity consumption, the extreme historical electricity consumption will be replaced by the mean value of historical electricity consumption;

[0034] If there is no extreme historical electricity consumption within the standardized historical electricity consumption, no operation will be performed.

[0035] Furthermore, the analysis process of the demand forecasting module also includes:

[0036] Recording the historical power consumption with the timestamp after the abnormal value detection is completed as the first historical power consumption;

[0037] Inputting the first historical electricity consumption into the pre-trained deep learning model;

[0038] The deep learning model then outputs electricity demand forecast data.

[0039] Furthermore, the working process of the electric energy scheduling module is as follows:

[0040] Obtain the real-time battery capacity data SSR and standard battery capacity data BZR of the electric energy storage module, and the real-time photovoltaic power generation SSF of the photovoltaic power generation terminal;

[0041] Then, the power consumption data YDLt of any time period in the application power demand forecast data of the power storage module is obtained, where t is the number of the time period.

[0042] Furthermore, the working process of the electric energy scheduling module also includes:

[0043] When the real-time photovoltaic power generation of the photovoltaic power generation terminal is greater than the power consumption data of any time period in the power demand forecast data, the photovoltaic power generation terminal directly outputs electric energy and outputs the excess electric energy to the electric energy storage module;

[0044] The charging value CD of the electric energy storage module is calculated by the formula, and the formula is as follows:

[0045] CD=min(SSF-YDLt, BZR-SSR); the formula indicates that the charging value CD of the electric energy storage module takes the minimum value of SSF-YDLt and BZR-SSR.

[0046] Furthermore, the working process of the electric energy scheduling module also includes:

[0047] When the real-time photovoltaic power generation of the photovoltaic power generation terminal is less than or equal to the power consumption data of any time period in the power demand forecast data;

[0048] The discharge value FD of the electric energy storage module is calculated by the formula, and the specific formula is as follows:

[0049] FD=min(YDLt-SSF, SSR); the formula indicates that the discharge value FD of the electric energy storage module takes the minimum value between YDLt-SSF and SSR. When the real-time photovoltaic power generation of the photovoltaic power generation terminal is less than or equal to the power consumption data of any time period in the power demand forecast data, the photovoltaic power generation terminal outputs electric energy to the electric energy storage module, and then the electric energy storage module outputs electric energy.

[0050] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0051] 1. The present invention uses a health monitoring module to monitor the health status of the energy storage module in real time, monitors and analyzes the energy storage data of the energy storage module, obtains the health status of the energy storage module, and maintains the energy storage module according to the health status of the energy storage module;

[0052] 2. The present invention utilizes a demand forecasting module to analyze the historical electricity consumption of the electric energy storage module, obtains the electricity demand forecast data of the electric energy storage module through analysis and sends it to the electric energy dispatching module. The electric energy dispatching module dynamically adjusts the electric energy storage capacity of the electric energy storage module according to the real-time photovoltaic power generation and electricity demand forecast data of the photovoltaic power generation terminal, thereby realizing the optimized control of electricity storage of the photovoltaic new energy power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0054] Figure 1 is the overall system block diagram of the present invention;

[0055] Figure 2 This is a working principle diagram of the demand forecasting module in the present invention;

[0056] Figure 3 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0057] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Embodiment 1:

[0059] See also Figure 1-Figure 2 As shown, the technical solution provided by the present invention is: applicable to the photovoltaic new energy power system power storage optimization control system, including a data acquisition module, an electric energy storage module, an electric energy dispatching module, a demand forecasting module, a health monitoring module, a database module, a photovoltaic power generation terminal and a management platform;

[0060] In this embodiment, the photovoltaic power generation terminal is used to convert light energy into electrical energy and transmit the converted electrical energy to the electrical energy storage module;

[0061] It should be specifically stated that the photovoltaic power generation terminal is composed of multiple silicon-based batteries; the small energy storage battery in the photovoltaic power generation terminal is used to reduce the power fluctuation caused by the instantaneous fluctuation of power output during the photovoltaic power generation process, and at the same time can improve the power conversion efficiency when converting light energy into electrical energy;

[0062] Specifically, when sunlight shines on the silicon-based battery, the silicon-based battery converts light energy into electrical energy and stores it in a small energy storage battery in the photovoltaic power generation terminal. The photovoltaic power generation terminal matches the voltage and current output by the small energy storage battery with the standard output voltage and standard output current through a DC / DC converter. After the matching is completed, the small energy storage battery outputs the stored electrical energy to the electrical energy storage module;

[0063] The management platform is connected to a plurality of electric energy storage modules, and the electric energy storage modules are used to store the electric energy transmitted by the photovoltaic power generation terminal;

[0064] It should be specifically noted that the large energy storage battery used by the energy storage module to store electric energy can be a lead-acid battery, a nickel battery, a lithium battery or a sodium-sulfur battery, and the user or staff can select it according to their needs;

[0065] Specifically, the energy storage module is composed of a large energy storage battery and a DC / AC inverter. The electric energy transmitted by the photovoltaic power generation terminal is direct current. When the energy storage module receives the electric energy transmitted from the photovoltaic power generation terminal, the energy storage module inputs the electric energy into the large energy storage battery for electric energy storage; when the management platform sends an output electric energy instruction to the energy storage module, the large energy storage battery converts the stored electric energy into alternating current through the DC / AC inverter for electric energy output.

[0066] In a specific implementation, the data acquisition module is used to collect the electric energy storage data of the electric energy storage module, and then upload the electric energy storage data to the management platform;

[0067] It should be specifically noted that the electric energy storage data of the electric energy storage module includes real-time current data, real-time temperature data, real-time voltage data and real-time battery capacity data of the electric energy storage module;

[0068] The management platform sends the electric energy storage data of the electric energy storage module to the health monitoring module;

[0069] Furthermore, the health monitoring module is used to monitor the health status of the large energy storage battery in the electric energy storage module in real time, and monitor and analyze the electric energy storage data of the electric energy storage module. The health monitoring module is connected to a database module, and the database module stores standard voltage data, standard temperature data and standard battery capacity data of the electric energy storage module. The monitoring and analysis process of the health monitoring module is as follows:

[0070] Step P1, obtain the real-time current data SSI, real-time voltage data SSV and standard voltage data BZV of the electric energy storage module, and calculate the internal resistance degradation index NZZ of the large energy storage battery in the electric energy storage module through the formula, the specific formula is as follows:

[0071] NZZ=|BZV-SSV| / SSI;

[0072] Step P2, obtaining the real-time temperature data SST and the standard temperature data BZT of the electric energy storage module, and calculating the temperature health index WDJ of the large energy storage battery in the electric energy storage module by the formula, the specific formula is as follows:

[0073] WDJ=SST / BZT;

[0074] Step P3, obtain the real-time battery capacity data SSR and the standard battery capacity data BZR of the electric energy storage module, and calculate the capacity health index JKR of the large energy storage battery through the formula, the specific formula is as follows:

[0075] JKR=SSR / BZR;

[0076] Step P4, calculate the comprehensive health index ZHJ of the electric energy storage module through a formula, the specific formula is as follows:

[0077] ZHJ=w1×JKR+w2×(1 / NZZ)+w3×WDJ, where w1, w2 and w3 are weight coefficients with fixed values, and w1>w2>w3;

[0078] Step P5, when the comprehensive health index of the electric energy storage module is greater than or equal to the first health threshold, the health monitoring module determines that the health status of the electric energy storage module is in a healthy state;

[0079] When the comprehensive health index of the electric energy storage module is greater than or equal to the second health threshold and less than the first health threshold, the health monitoring module determines that the health status of the electric energy storage module is in a sub-healthy state;

[0080] When the comprehensive health index of the electric energy storage module is less than the second health threshold, the health monitoring module determines that the health status of the electric energy storage module is in a risky state.

[0081] Wherein, the first health threshold is greater than the second health threshold, and the second health threshold is greater than zero;

[0082] The health monitoring module sends the health status of the electric energy storage module to the management platform, and the management platform is used to maintain the electric energy storage module according to the health status of the electric energy storage module.

[0083] Furthermore, the data acquisition module is also used to collect the historical power consumption of the electric energy storage module, and upload the historical power consumption to the management platform after adding a timestamp, and the management platform sends the historical power consumption with the timestamp to the demand forecasting module;

[0084] It should be specifically explained that the historical power consumption with timestamp is specifically as follows: the past year is divided into several continuous time periods, and then the power output data of the power storage module in each time period is obtained, and the power output data of each time period is combined to obtain the historical power consumption of the power storage module;

[0085] Specifically, the demand forecasting module is used to analyze the historical power consumption of the electric energy storage module to obtain the power demand forecasting data of the electric energy storage module. The analysis process of the demand forecasting module is as follows:

[0086] Step S1, normalizing the historical power consumption;

[0087] It should be specifically noted that normalization is an existing mature technology;

[0088] Step S2, performing abnormal value detection on the normalized historical power consumption. If there is extreme historical power consumption in the normalized historical power consumption, the extreme historical power consumption is replaced by the mean of the historical power consumption; if there is no extreme historical power consumption in the standardized historical power consumption, no operation is performed;

[0089] Step S3, recording the historical power consumption with the timestamp after abnormal value detection as the first historical power consumption;

[0090] Step S4: input the first historical electricity consumption into the pre-trained deep learning model, and then the deep learning model outputs electricity demand forecast data.

[0091] It should be specifically noted that the deep learning models used for demand forecasting include ARIMA autoregressive integrated moving average model, regression model, machine learning model and long short-term memory network. The system preferably uses ARIMA autoregressive integrated moving average model. The electricity demand forecast data is: the next year is divided into several consecutive time periods, and any time period contains a section of electricity demand forecast data. The section electricity demand forecast data is merged to obtain the electricity demand forecast data.

[0092] The demand forecasting module uploads the electricity demand forecasting data to the management platform, and the management platform sends the electricity demand forecasting data to the electric energy scheduling module.

[0093] In this embodiment, the data acquisition module is also used to collect the real-time photovoltaic power generation of the photovoltaic power generation terminal and upload it to the management platform, and the management platform sends the real-time photovoltaic power generation to the power dispatch module;

[0094] The electric energy dispatching module is used to dynamically adjust the electric energy storage capacity of the electric energy storage module according to the power demand forecast data, and the electric energy dispatching module is connected to the database module;

[0095] Specifically, the real-time battery capacity data SSR and the standard battery capacity data BZR of the electric energy storage module, the real-time photovoltaic power generation SSF of the photovoltaic power generation terminal and the power consumption data YDLt of any time period in the power demand forecast data are obtained, where t is the number of the time period;

[0096] Furthermore, when the real-time photovoltaic power generation of the photovoltaic power generation terminal is greater than the power consumption data in any time period in the power demand forecast data, the photovoltaic power generation terminal directly outputs electric energy, and at the same time outputs the excess electric energy to the electric energy storage module, and the charging value CD of the electric energy storage module is calculated by the formula, and the specific formula is as follows:

[0097] CD=min(SSF-YDLt, BZR-SSR);

[0098] It should be specifically noted that the above formula indicates that the charge value CD of the electric energy storage module takes the minimum value of SSF-YDLt and BZR-SSR, which is used to ensure that the electric energy transmitted from the photovoltaic power generation terminal to the electric energy storage module does not exceed the remaining capacity of the large energy storage battery;

[0099] Furthermore, when the real-time photovoltaic power generation of the photovoltaic power generation terminal is less than or equal to the power consumption data of any time period in the power demand forecast data, the discharge value FD of the electric energy storage module is calculated by the formula, and the specific formula is as follows:

[0100] FD = min (YDLt-SSF, SSR);

[0101] It should be specifically noted that the above formula indicates that the discharge value FD of the electric energy storage module takes the minimum value between YDLt-SSF and SSR. When the real-time photovoltaic power generation of the photovoltaic power generation terminal is less than or equal to the power consumption data of any time period in the power demand forecast data, the photovoltaic power generation terminal outputs electric energy to the electric energy storage module, and then the electric energy storage module outputs electric energy;

[0102] The electric energy dispatching module uploads the charging value CD of the electric energy storage module or the discharging value FD of the electric energy storage module to the management platform, and the management platform is used to check the charging and discharging status of the electric energy storage module according to the charging value or the discharging value.

[0103] Due to the adoption of the technical solution of this embodiment, the health status of the electric energy storage module is monitored in real time through the health monitoring module, and the electric energy storage data of the electric energy storage module is monitored and analyzed to obtain the health status of the electric energy storage module, and the electric energy storage module is maintained according to the health status of the electric energy storage module;

[0104] At the same time, the demand forecasting module is used to analyze the historical electricity consumption of the electric energy storage module, and the electricity demand forecast data of the electric energy storage module is obtained and sent to the electric energy dispatching module. The electric energy dispatching module dynamically adjusts the electric energy storage capacity of the electric energy storage module according to the real-time photovoltaic power generation and electricity demand forecast data of the photovoltaic power generation terminal, so as to realize the optimized control of electricity storage of the photovoltaic new energy power system.

[0105] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0106] Embodiment 2

[0107] like Figure 3 As shown, based on another concept of the same invention, a method for optimizing the control of power storage in a photovoltaic new energy power system is proposed, and the specific method is as follows:

[0108] Step S10, the photovoltaic power generation terminal converts light energy into electrical energy, and transmits the converted electrical energy to the electrical energy storage module for storage;

[0109] Step S20, the data acquisition module acquires the electric energy storage data of the electric energy storage module, and then obtains the standard voltage data, standard temperature data and standard battery capacity data of the electric energy storage module;

[0110] Step S30, real-time monitoring of the health status of the energy storage module is performed through the health monitoring module, and the energy storage data of the energy storage module is monitored and analyzed to obtain the health status of the energy storage module, and the energy storage module is maintained according to the health status of the energy storage module;

[0111] Step S40, the data acquisition module also collects the historical power consumption of the electric energy storage module and sends it to the demand prediction module, uses the demand prediction module to analyze the historical power consumption of the electric energy storage module, analyzes to obtain the power demand prediction data of the electric energy storage module and sends it to the electric energy scheduling module;

[0112] Step S50, the data collection module also collects the real-time photovoltaic power generation of the photovoltaic power generation terminal and sends it to the power dispatching module, and the power dispatching module dynamically adjusts the power storage capacity of the power storage module according to the power demand forecast data.

[0113] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Applicable to photovoltaic new energy power system power storage optimization control system, characterized by: It includes data acquisition module, power storage module, power dispatch module, demand forecasting module, health monitoring module, database module, photovoltaic power generation terminal and management platform; The photovoltaic power generation terminal is used to convert light energy into electrical energy and transmit the converted electrical energy to the electrical energy storage module; the electrical energy storage module is used to store the electrical energy transmitted by the photovoltaic power generation terminal; The data acquisition module is used to collect the electric energy storage data of the electric energy storage module and send it to the health monitoring module via the management platform; The database module is used to store standard voltage data, standard temperature data and standard battery capacity data of the electric energy storage module; the health monitoring module is used to monitor the health status of the electric energy storage module in real time, and monitor and analyze the electric energy storage data of the electric energy storage module, and obtain the health status of the electric energy storage module and send it to the management platform, and the management platform is used to maintain the electric energy storage module according to the health status of the electric energy storage module; The monitoring and analysis process of the health monitoring module is as follows: Obtaining real-time current data SSI, real-time voltage data SSV and standard voltage data BZV of the electric energy storage module; The internal resistance degradation index NZZ of the large energy storage battery in the electric energy storage module is calculated by the formula NZZ=|BZV-SSV| / SSI; Obtaining real-time temperature data SST and standard temperature data BZT of the electric energy storage module; The temperature health index WDJ of the large energy storage battery in the energy storage module is calculated by the formula WDJ = SST / BZT; Obtain real-time battery capacity data SSR and standard battery capacity data BZR of the electric energy storage module; The capacity health index JKR of large energy storage batteries is calculated by the formula JKR=SSR / BZR; The comprehensive health index ZHJ of the energy storage module is then calculated using the formula ZHJ=w1×JKR+w2×(1 / NZZ)+w3×WDJ; where w1, w2 and w3 are weight coefficients with fixed values, and w1>w2>w3; When the comprehensive health index of the electric energy storage module is greater than or equal to a first health threshold, determining that the health status of the electric energy storage module is a healthy status; When the comprehensive health index of the electric energy storage module is greater than or equal to the second health threshold and less than the first health threshold, it is determined that the health status of the electric energy storage module is in a sub-healthy state; When the comprehensive health index of the electric energy storage module is less than the second health threshold, the health status of the electric energy storage module is determined to be a risky state; wherein the first health threshold is greater than the second health threshold, and the second health threshold is greater than zero; The data acquisition module is also used to collect the historical power consumption of the electric energy storage module, and upload the historical power consumption to the management platform after adding a timestamp, and the management platform sends the historical power consumption with the timestamp to the demand forecasting module; The demand forecasting module is used to analyze the historical power consumption of the power storage module, and obtain the power demand forecasting data of the power storage module through the analysis, and the power demand forecasting data is sent to the power dispatching module through the management platform; the data acquisition module is also used to collect the real-time photovoltaic power generation of the photovoltaic power generation terminal, and the real-time photovoltaic power generation is sent to the power dispatching module through the management platform; The electric energy scheduling module is used to dynamically adjust the electric energy storage capacity of the electric energy storage module according to the power demand forecast data, obtain the charging value or discharging value of the electric energy storage module and upload it to the management platform, and the management platform is used to check the charging and discharging status of the electric energy storage module based on the charging value or discharging value.

2. The power storage optimization control system applicable to photovoltaic new energy power system according to claim 1 is characterized in that: The electric energy storage data of the electric energy storage module includes real-time current data, real-time temperature data, real-time voltage data and real-time battery capacity data of the electric energy storage module.

3. The power storage optimization control system applicable to photovoltaic new energy power system according to claim 2 is characterized in that: The historical electricity consumption with timestamp is specifically as follows: the past year is divided into several continuous time periods, and then the power output data of the power storage module in each time period is obtained, and the power output data of each time period is combined to obtain the historical electricity consumption of the power storage module.

4. The power storage optimization control system applicable to photovoltaic new energy power system according to claim 3 is characterized in that: The analysis process of the demand forecasting module is as follows: Normalize the historical electricity consumption; Perform outlier detection on normalized historical electricity consumption; If there is extreme historical electricity consumption in the normalized historical electricity consumption, the extreme historical electricity consumption will be replaced by the mean value of historical electricity consumption; If there is no extreme historical electricity consumption within the standardized historical electricity consumption, no operation will be performed.

5. The power storage optimization control system applicable to photovoltaic new energy power system according to claim 4 is characterized in that: The analysis process of the demand forecasting module also includes: Recording the historical power consumption with the timestamp after the abnormal value detection is completed as the first historical power consumption; Inputting the first historical electricity consumption into the pre-trained deep learning model; The deep learning model then outputs electricity demand forecast data.

6. The power storage optimization control system applicable to photovoltaic new energy power system according to claim 5 is characterized in that: The working process of the electric energy scheduling module is as follows: Obtain the real-time battery capacity data SSR and standard battery capacity data BZR of the electric energy storage module, and the real-time photovoltaic power generation SSF of the photovoltaic power generation terminal; Then, the power consumption data YDLt of any time period in the application power demand forecast data of the power storage module is obtained, where t is the number of the time period.

7. The power storage optimization control system applicable to photovoltaic new energy power system according to claim 6 is characterized in that: The working process of the electric energy scheduling module also includes: When the real-time photovoltaic power generation of the photovoltaic power generation terminal is greater than the power consumption data of any time period in the power demand forecast data, the photovoltaic power generation terminal directly outputs electric energy and outputs the excess electric energy to the electric energy storage module; The charging value CD of the electric energy storage module is calculated by the formula, and the formula is as follows: CD=min(SSF-YDLt, BZR-SSR); the formula indicates that the charging value CD of the electric energy storage module takes the minimum value of SSF-YDLt and BZR-SSR.

8. The power storage optimization control system applicable to photovoltaic new energy power system according to claim 7 is characterized in that: The working process of the electric energy scheduling module also includes: When the real-time photovoltaic power generation of the photovoltaic power generation terminal is less than or equal to the power consumption data of any time period in the power demand forecast data; The discharge value FD of the electric energy storage module is calculated by the formula, and the specific formula is as follows: FD=min(YDLt-SSF, SSR); the formula indicates that the discharge value FD of the electric energy storage module takes the minimum value between YDLt-SSF and SSR. When the real-time photovoltaic power generation of the photovoltaic power generation terminal is less than or equal to the power consumption data of any time period in the power demand forecast data, the photovoltaic power generation terminal outputs electric energy to the electric energy storage module, and then the electric energy storage module outputs electric energy.

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